NVIDIA80 GB VRAM2,000 GB/s$1.2/hr

Qwen 2.5-VL 72B (VLM) on A100 80GB SXM4: Does It Fit, How Fast, What It Costs

Qwen 2.5-VL 72B (VLM) at FP8 (E4M3) needs 72.0 GB for its weights and 93.5 GB in total at 8K context with 8 concurrent users. That means it needs 2× A100 80GB SXM4. Every figure is computed by tokcalc's open-source capacity model.

Fits?
Yes, 2×
93.5 GB needed / 160 GB available
Decode speed
46.04
tok/s per stream
Aggregate
1105
tok/s @ 8 concurrent
Cost / 1M tokens
$0.6033
2× A100 80GB SXM4
What changes as you add A100 80GB SXM4s
Tensor-parallel degree vs VRAM, throughput, and cost. Cost scales linearly with GPU count; decode throughput scales near-linearly too, so cost per token stays roughly flat.
GPUsTotal VRAMVRAM neededDecode tok/sAggregate tok/sTTFTCost / 1M tokFits?
1× A10080 GB93.5 GB0.0000.000——✗
2× A100160 GB93.5 GB46.0411053.83 s$0.6033✓
4× A100320 GB93.5 GB92.0844201.93 s$0.3017✓
8× A100640 GB93.5 GB184.217680980.7 ms$0.1508✓
Concurrency vs context length
On 2× A100 80GB SXM4 at FP8 (E4M3). KV cache scales linearly with context, so this is the number of users your GPUs can actually serve.
ContextConcurrent usersKV per requestFits?
4K651.34 GB✓ fits
8K322.68 GB✓ fits
33K810.74 GB✓ fits
66K421.47 GB✓ fits
131K242.95 GB✓ fits
Other GPUs that run Qwen 2.5-VL 72B (VLM)
Same workload, same quantization — ranked by value (tokens per dollar-hour).
GPUGPUsAggregate tok/sCost / 1M tok
RTX 5090 32GB4×3960$0.1403
H100 SXM5 80GB2×1851$0.7504
AMD MI300X 192GB1×861.3$0.8063
L40S 48GB2×477.4$0.8147
AMD MI325X 288GB1×975.0$0.9972
Intel Gaudi 3 128GB1×536.3$1.04
Frequently asked questions

Can Qwen 2.5-VL 72B (VLM) run on A100 80GB SXM4?

Yes, but it needs 2× A100 80GB SXM4. At FP8 (E4M3) the total footprint is 93.5 GB (72.0 GB of weights plus 21.5 GB of KV cache at 8K × 8 concurrent users), against 160 GB of available VRAM across 2 GPUs.

How fast is Qwen 2.5-VL 72B (VLM) on A100 80GB SXM4?

46.04 tokens/sec per stream and 1105 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 3.83 s, and inter-token latency is 21.7 ms.

What does Qwen 2.5-VL 72B (VLM) on A100 80GB SXM4 cost per million tokens?

$0.6033 per 1M output tokens at 2× A100 80GB SXM4 ($1.20/hr). That is the marginal hardware cost — add idle capacity, ops, and egress for the real figure.

How much VRAM does Qwen 2.5-VL 72B (VLM) need on A100 80GB SXM4?

72.0 GB for the weights at FP8 (E4M3), plus 21.5 GB of KV cache for 8 concurrent requests at 8K context — 93.5 GB total. Every extra concurrent user at long context costs more, so leave headroom.

Is A100 80GB SXM4 the right choice for Qwen 2.5-VL 72B (VLM)?

It works. Compare $0.6033 per 1M tokens against the alternatives below — the cheapest option that fits the same workload is 8× RTX 4080 16GB at $0.1052, and the fastest is 4× Cerebras CS-3 (WSE-3) at 44200 tok/s.

Adjust the workload yourself

Same formulas, your context length, concurrency, and quantisation.

Open in calculator